info:eu-repo/semantics/bachelorThesis
Obtención de una base de datos de temperatura, corriente y caída de tensión causada por el desbalanceo mecánico en el laboratorio de diagnóstico técnico
Fecha
2022-06-09Registro en:
Guzmán Cullay, Edwin Patricio. (2022). Obtención de una base de datos de temperatura, corriente y caída de tensión causada por el desbalanceo mecánico en el laboratorio de diagnóstico técnico. Escuela Superior Politécnica de Chimborazo. Riobamba.
Autor
Guzmán Cullay, Edwin Patricio
Resumen
The main objective of this work was to obtain a database by collecting measurements of temperature, current and voltage drop caused by mechanical imbalance, using a module suitable for this work. It is located in Technical Diagnosis and Energy Efficiency Laboratory at Escuela Superior Politécnica de Chimborazo "ESPOCH". The distribution of the measurements corresponds to (30/70), 30% of the measurements were made with the module aligned without unbalance masses and 70% were made for 5 different masses that cause static unbalance. The equipment used were: a thermographic camera, an energy and electrical quality analyzer, with which measurements were made every 30 seconds; For the analysis of the data obtained, the Python program was used in which the lines of code were programmed, obtaining graphs with relevant information on the data analyzed. As a result of the measurements with the equipment, a database consisting of 2000 rows by 102 columns was obtained, with a total of 204,000 data. The histograms of the variables analyzed serve to identify the behavior of the data when the module is without unbalanced and unbalanced masses. In conclusion, the temperature increase that exists in the points (C1, C2, C3 and C4) allows to identify that the module presents mechanical imbalance. There is a positive correlation in the temperature dispersion diagram, the more the imbalance increases more the temperature of the analyzed point’s increases. There is also an increase in the values of current, voltage, tension and power that allows to show an imbalance in the module. It is recommended to carry out future work related to Machine Learning or machine learning in the detection of imbalance in rotating equipment.